Ron D. Appel

Protein identification and analysis tools in the ExPASy server.

Protein identification and analysis software performs a central role in the investigation of proteins from two-dimensional (2-D) gels and mass spectrometry. For protein identification, the user matches certain empirically acquired information against a protein database to define a protein as already known or as novel. For protein analysis, information in protein databases can be used to predict certain properties about a protein, which can be useful for its empirical investigation. The two processes are thus complementary. Although there are numerous programs available for those applications, we have developed a set of original tools with a few main goals in mind. Specifically, these are: 1. To utilize the extensive annotation available in the Swiss-Prot database wherever possible, in particular the position-specific annotation in the Swiss-Prot feature tables to take into account posttranslational modifications and protein processing. 2. To develop tools specifically, but not exclusively, applicable to proteins prepared by two dimensional gel electrophoresis and peptide mass fingerprinting experiments. 3. To make all tools available on the World-Wide Web (WWW), and freely usable by the scientific community. In this chapter we give details about protein identification and analysis software that is available through the ExPASy World Wide Web server.

ExPASy: the proteomics server for in-depth protein knowledge and analysis

The ExPASy (the Expert Protein Analysis System) World Wide Web server (http://www.expasy.org), is provided as a service to the life science community by a multidisciplinary team at the Swiss Institute of Bioinformatics (SIB). It provides access to a variety of databases and analytical tools dedicated to proteins and proteomics. ExPASy databases include SWISS-PROT and TrEMBL, SWISS-2DPAGE, PROSITE, ENZYME and the SWISS-MODEL repository. Analysis tools are available for specific tasks relevant to proteomics, similarity searches, pattern and profile searches, post-translational modification prediction, topology prediction, primary, secondary and tertiary structure analysis and sequence alignment. These databases and tools are tightly interlinked: a special emphasis is placed on integration of database entries with related resources developed at the SIB and elsewhere, and the proteomics tools have been designed to read the annotations in SWISS-PROT in order to enhance their predictions. ExPASy started to operate in 1993, as the first WWW server in the field of life sciences. In addition to the main site in Switzerland, seven mirror sites in different continents currently serve the user community.

Guidelines for the next 10 years of proteomics

In the last ten years, the field of proteomics has expanded at a rapid rate. A range of exciting new technology has been developed and enthusiastically applied to an enormous variety of biological questions. However, the degree of stringency required in proteomic data generation and analysis appears to have been underestimated. As a result, there are likely to be numerous published findings that are of questionable quality, requiring further confirmation and/or validation. This manuscript outlines a number of key issues in proteomic research, including those associated with experimental design, differential display and biomarker discovery, protein identification and analytical incompleteness. In an effort to set a standard that reflects current thinking on the necessary and desirable characteristics of publishable manuscripts in the field, a minimal set of guidelines for proteomics research is then described. These guidelines will serve as a set of criteria which editors of PROTEOMICS will use for assessment of future submissions to the Journal.

The Health On the Net Code of Conduct for medical and health Websites

Internet has become one of the most used communication media. This and the fact that no constraining information publishing policy exists have created an urgent need to control the quality of information circulating through this media. To this purpose, the Health On the Net Foundation has initiated the Code of Conduct (HONcode) for the health/medical domain. This initiative proposes guidelines to information providers, with the aim, on the one hand, of raising the quality of data available on the Net and, on the other hand, of helping to identify Internet sites that are maintained by qualified people and contain reliable data. The HONcode mainly includes the following ethical aspects: the authors credentials, the date of the last modification with respect to clinical documents, confidentiality of data, source data reference, funding and the advertising policy. This article presents the HONcode and its evolution since it was launched in 1996.

The mouse SWISS‐2D PAGE database: a tool for proteomics study of diabetes and obesity

A number of two‐dimensional electrophoresis (2‐DE) reference maps from mouse samples have been established and could be accessed through the internet. An up‐to‐ date list can be found in WORLD‐2D PAGE (http://www.expasy.ch/ch2d/2d‐index.html), an index of 2‐DE databases and services. None of them were established from mouse white and brown adipose tissues, pancreatic islets, liver nuclei and skeletal muscle. This publication describes the mouse SWISS‐2D PAGE database. Proteins present in samples of mouse (C57Bl/6J) liver, liver nuclei, muscle, white and brown adipose tissue and pancreatic islets are assembled and described in an accessible uniform format. SWISS‐2D PAGE can be accessed through the World Wide Web (WWW) network on the ExPASy molecular biology server (http://www.expasy.ch/ch2d/).

Improving protein identification from peptide mass fingerprinting through a parameterized multi-level scoring algorithm and an optimized peak detection.

We have developed a new algorithm to identify proteins by means of peptide mass fingerprinting. Starting from the matrix‐assisted laser desorption/ionization‐time‐of‐flight (MALDI‐TOF) spectra and environmental data such as species, isoelectric point and molecular weight, as well as chemical modifications or number of missed cleavages of a protein, the program performs a fully automated identification of the protein. The first step is a peak detection algorithm, which allows precise and fast determination of peptide masses, even if the peaks are of low intensity or they overlap. In the second step the masses and environmental data are used by the identification algorithm to search in protein sequence databases (SWISS‐PROT and/or TrEMBL) for protein entries that match the input data. Consequently, a list of candidate proteins is selected from the database, and a score calculation provides a ranking according to the quality of the match. To define the most discriminating scoring calculation we analyzed the respective role of each parameter in two directions. The first one is based on filtering and exploratory effects, while the second direction focuses on the levels where the parameters intervene in the identification process. Thus, according to our analysis, all input parameters contribute to the score, however with different weights. Since it is difficult to estimate the weights in advance, they have been computed with a generic algorithm, using a training set of 91 protein spectra with their environmental data. We tested the resulting scoring calculation on a test set of ten proteins and compared the identification results with those of other peptide mass fingerprinting programs.

The 1999 SWISS-2DPAGE database update

SWISS-2DPAGE (http://www.expasy.ch/ch2d/ ) is an annotated two-dimensional polyacrylamide gel electro-phoresis (2-DE) database established in 1993. The current release contains 24 reference maps from human and mouse biological samples, as well as from Saccharomyces cerevisiae, Escherichia coli and Dictyostelium discoideum origin. These reference maps have now 2824 identified spots, corresponding to 614 separate protein entries in the database, in addition to virtual entries for each SWISS-PROT sequence or any user-entered amino acids sequence. Last year improvements in the SWISS-2DPAGE database are as follows: three new maps have been created and several others have been updated; cross-references to newly built federated 2-DE databases have been added; new functions to access the data have been provided through the ExPASy proteomics server.

Proteomics and its trends facing nature's complexity

The complexity of nature is tremendous, particularly at the epigenetic level. Proteomic studies must therefore complement genomic discoveries to better understand biological processes. Because of the very large number of modified proteins and their great variability in physico‐chemical properties, no single method can be used to analyze all of them. Mass spectrometry has demonstrated its superior ability to rapidly identify and partially characterize numerous proteins in low abundance and has become a central element in most proteomic projects. Studies of protein function are necessary to understand biological pathways and this is being tackled using several approaches such as two hybrid systems, phage technology or affinity methods. Finally, mathematical and bioinformatic developments will be essential to study natures complex systems.

New perspectives in the Escherichia coli proteome investigation.

Escherichia coli is a model organism for biochemical and biological studies as it is one of the best characterised prokaryote. Two‐dimensional polyacrylamide gel electrophoresis, computer image analysis and different protein identification techniques gave rise, in 1995, to the Escherichia coli SWISS‐2D PAGE database (http://www.expasy.ch/ch2d/). In the E. coli 3.5–10 SWISS‐2D PAGE map, 40% of the E. coli proteome was displayed. The present study demonstrated that the use of narrow range pH gradients is able to potentially display up to a few copies of protein per E. coli cell. Moreover, the six new E. coli SWISS‐2D PAGE maps (pH 4–5, 4.5–5.5, 5–6, 5.5–6.7, 6–9 and 6–11) presented here displayed altogether more than 70% of the entire E. coli proteome.

Modeling peptide mass fingerprinting data using the atomic composition of peptides.

The peptide mass fingerprinting technique is commonly used for identifying proteins analyzed by mass spectrometry (MS) after enzymatic digestion. Our goal is to build a theoretical model that predicts the mass spectra of such digestion products in order to improve the identification and characterization of proteins using this technique. We present here the first step towards a full MS model. We have modeled MS spectra using the atomic composition of peptides and evaluated the influence that this composition may have on the MS signals. Peptides deduced from the SWISS‐PROT protein sequence database were used for the calculation. To validate the model, the variability of the peptide mass distribution in SWISS‐PROT was compared to two theoretical, randomly generated databases. Functions have been built that describe the behavior of the isotopic distribution according to the mass of peptides. The variability of these functions was analyzed. In particular, the influence of sulfur was studied. This work, while representing only a first step in the construction of an MS model, yields immediate practical results, as the new isotopic distribution model significantly improves peak detection in MS spectra used by protein identification algorithms.